무용예술에서 생성형 인공지능의 역할과 가능성 - CNN, RNN 및 GAN 기술의 적용
The Role and Potential of Generative AI in Dance - Application of CNN, RNN, and GAN Technologies
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초록
본 연구에서는 생성형 인공지능의 기능적 특성을 분석하여 무용예술 도구로서의 역할과 가능성을 탐색하고자 한다. 데이터의 패턴 분석과 예측에 사용되는 인공신경망 모델인 CNN과 RNN, 그리고 원래 이미지 생성에 사용되었으나 현재는 움직임 생성까지 적용 범위를 확장하고 있는 GAN의 기능적 구조를 검토한다. 이를 통해 생성형 인공지능이 안무 생성, 무용 훈련, 무보 기록 체계 개선 등에 어떠한 혁신적 변화를 가져올 수 있는지 논의한다. 궁극적으로, 생성형 인공지능 기술의 급격한 발전에 따라 그 영향력이 더욱 확대될 것으로 예상되므로 무용 창작가들에게 이 기술을 실질적으로 활용할 수 있는 유용한 방향성을 제공하고자 한다.
Abstract
This study explores the role and potential of generative AI as a tool in dance by analyzing key neural network models, including CNN, RNN, and GAN. Initially used for image generation, GANs now extend to movement generation. The research discusses how these models can innovate choreography, dancer training, technical movement analysis, and dance notation. As generative AI rapidly evolves, its impact is expected to grow, and this study aims to provide practical direction for dance creators to effectively utilize these technologies in their work.
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